{"id":11914,"date":"2026-04-22T07:57:45","date_gmt":"2026-04-22T07:57:45","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/11914\/"},"modified":"2026-04-22T07:57:45","modified_gmt":"2026-04-22T07:57:45","slug":"google-deepmind-ceo-says-the-memory-shortage-is-creating-an-ai-choke-point","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/11914\/","title":{"rendered":"Google Deepmind CEO says the memory shortage is creating an AI ‘choke point’"},"content":{"rendered":"\n

Google DeepMind CEO Demis Hassabis<\/a> said that the “whole supply chain” for memory chips is constrained.<\/p>\n

“You need a lot of chips to be able to experiment on new ideas,” Hassabis told CNBC.<\/p>\n

Google produces its own TPUs, but Hassabis said that there were still “key components” that were supply-constrained.<\/p>\n

The memory shortage<\/a> takes no prisoners. Even Google isn’t immune.<\/p>\n

AI companies are duking it out for greater and greater quantities of memory chips. The problem? The industry is heavily supply-constrained. Costs have skyrocketed, products have been tied up, and some companies \u2014 especially those in consumer electronics<\/a> \u2014 are increasing prices.<\/p>\n

On the AI front, Google DeepMind CEO Demis Hassabis told CNBC<\/a> that physical challenges were “constraining a lot of deployment.” Google sees “so much more demand” for Gemini and its other models than it could serve, he said.<\/p>\n

“Also, it does constrain a little bit the research,” Hassabis said. “You need a lot of chips to be able to experiment on new ideas at a big enough scale that you can actually see if they’re going to work.”<\/p>\n

Researchers want chips, whether they work at Google, Meta, OpenAI, or other Big Tech companies, and memory is a key component. Mark Zuckerberg said that AI researchers demanded two things<\/a> beyond money: the fewest number of people reporting to them, and the most chips possible.<\/p>\n

Hassabis said that wherever there was a capacity constraint, there was a “choke point.”<\/p>\n

“The whole supply chain is kind of strained,” Hassabis said. “We’re lucky, because we have our own TPUs, so we have our own chip designs.”<\/p>\n

Google has long built TPUs<\/a> \u2014 Tensor Processing Units \u2014 for internal use. The company also leases them to external customers through its cloud, which has also put Nvidia on edge<\/a>.<\/p>\n

But even access to their own TPUs won’t save Google from having to navigate the highly competitive memory market. “It still, in the end, actually comes down to a few suppliers of a few key components,” Hassabis said.<\/p>\n

Three suppliers<\/a> dominate memory chip production: Samsung, Micron, and SK Hynix. These companies are struggling to meet demand for chips from AI hyperscalers without dropping their longtime electronics customers.<\/p>\n

It doesn’t help that AI companies mainly want a different type of memory chip than PC manufacturers do. Large language model producers want HBM (high-bandwidth memory) chips.<\/p>\n

Don’t expect Google’s spending on AI infrastructure and chips to go down anytime soon. On its fourth-quarter earnings call, the company projected capital expenditures<\/a> of $175 billion to $185 billion for 2026.<\/p>\n

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